Sci—Thur PM: YIS — 02: Intraoperative Guidance for Minimally Invasive Abdominal Surgery Using Fused Video and Ultrasound Images: A Phantom Study
Bibliographic record
Abstract
Many abdominal surgery procedures are now performed minimally invasively. We consider tumour resection, where surgeons use a laparoscopic camera to view the organ surface and a laparoscopic ultrasound (US) probe to visualize the tumour to plan and perform the excision. Conventionally, images are displayed separately and are typically presented in 2D. Therefore, the surgeon has to look back and forth between the images and mentally map the US onto the video to determine the tumour location relative to the surface. Furthermore, the 2D nature of the images decreases depth perception. To address these limitations, we developed an augmented reality visualization that fuses images in a common 3D environment. Instruments were tracked using sensors spatially identified with a magnetic field generator. Through calibration, their image locations were determined in real time. The accuracy of the camera and US calibrations was determined both relative to the tracking system and to each other using target localization. We evaluated the efficacy of the fusion with a phantom experiment. A surgeon performed tumour resections on polyvinyl alcohol‐cryogel phantoms under the guidance of the conventional visualization and the fusion system presented in 2D and in 3D. The target localization error was 1.20±0.08mm for the camera, 1.85±0.14mm for the US, and 2.38±0.11mm between the camera and the US. Early results demonstrate a faster resection planning time using fusion compared to the conventional setup while maintaining similar margin accuracy. This study supports the implementation of fusion for guidance of time‐sensitive resection tasks performed under conditions of warm ischemia.
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How this classification was reachedexpand
Full frame machine prediction
Teacher imitationNot calibrated prevalence, not ground truth. Human validation pending. The Gemma side is a direct model label for every work in the frame, read from the title-only record. The Codex side is a classifier learned from the 10,348 direct Codex labels and calibrated to design-weighted sample rates; fields without enough sample support carry no Codex call. Candidate is the union of the two sides; consensus is their intersection. These outputs are machine_predicted_unvalidated and are not human labels.
Distilled classifier scores by category (both heads)
| Category | Codex | Gemma |
|---|---|---|
| Metaresearch | 0.001 | 0.001 |
| Meta-epidemiology (narrow) | 0.000 | 0.000 |
| Meta-epidemiology (broad) | 0.000 | 0.000 |
| Bibliometrics | 0.001 | 0.000 |
| Science and technology studies | 0.000 | 0.000 |
| Scholarly communication | 0.001 | 0.001 |
| Open science | 0.000 | 0.001 |
| Research integrity | 0.001 | 0.000 |
| Insufficient payload (model declined to judge) | 0.010 | 0.002 |
Machine scores (provisional)
The two teacher heads of the student model, read on this work. A score orders the frame for review; it never asserts a category, and the validation status ships verbatim with every row.
Baseline scores from an immature model (maturity gate not passed, 7 training rounds). Scores rank; they never assert a category.
score_only:v0-immature-baseline · verbatim from the scoring run: score_only means the number may rank works, and no category label ships from itClassification
machine, unvalidatedMachine predicted; a candidate call from one source (direct Gemma or distilled Codex), not a consensus.
How this classification was reached, model by model and score by score, is at the end of the page under "How this classification was reached".